A growing number of algorithms are being developed to automatically identify disorders or disease biomarkers from digitally recorded audio of patient speech. An important step in these analyses is to identify and isolate the patient's speech from that of other speakers or noise that are captured in a recording. However, current algorithms, such as diarization, only label the identified speech segments in terms of non-specific speakers, and do not identify the specific speaker of each segment, e.g., clinician and patient. In this paper, we present a novel algorithm that not only performs diarization on clinical audio, but also identifies the patient among the speakers in the recording and returns an audio file containing only the patient's speech. Our algorithm first uses pretrained diarization algorithms to separate the input audio into different tracks according to non-specific speaker labels. Next, in a novel step not conducted in other diarization tools, the algorithm uses the average loudness (quantified as power) of each audio track to identify the patient, and return the audio track containing only their speech. Using a practical expert-based evaluation methodology and a large dataset of clinical audio recordings, we found that the best implementation of our algorithm achieved near-perfect accuracy on two validation sets. Thus, our algorithm can be used for effectively identifying and isolating patient speech, which can be used in downstream expert and/or data-driven analyses.

The authors have declared no competing interest.

This work was supported by NIH grants R01 AG066471 and R01 HG011407. The technical parts of this work were supported in part by Oracle Cloud credits and related resources provided by the Oracle for Research program, as well as the computational resources and staff expertise provided by Scientific Computing at the Icahn School of Medicine at Mount Sinai.

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IRB of the Mount Sinai Health System gave ethical approval for this work.

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